Given that affine transformations are estimated during the feature matching stage, recent work has examined how these transformations might be used as extra information to improve image-based localization, visual odometry, and structure-from-motion. Köser developed a theory of relationship between homographies and ACs [1]}, and Bentolila and Francos demonstrated that an AC provides three constraints on the fundamental matrix [2]}. Raposo and Barreto re-derived these constraints [3]}, making them usable for essential matrix estimation using only two ACs instead of five PCs as is normally needed [4]}. Eichhardt and Chetverikov extended relative pose estimation using ACs to handle any central camera model [5]}.
Eichhardt and Barath [6]} demonstrated relative pose estimation from a single AC and a depth map estimated by a neural network.
Recently, Barath et al. provided recommendations for speeding up RANSAC when using ACs [7]}, including local feature geometry refinement and uncertainty propagation in RANSAC; these ideas could very well provide a further speedup in our approach.
